LMCache's distributed architecture supports cache sharing across multiple GPU devices or containerized environments, a feature that makes it particularly suited for large-scale AI inference deployments at the enterprise level. The system supports decentralized storage of cached data on multiple media, including GPU high-speed graphics memory (suitable for hot data), CPU DRAM (balancing speed and capacity), persistent disk storage (for cold data), and Redis clusters (supporting distributed access). Through intelligent data slicing and transfer mechanisms, different compute nodes can efficiently share cached key-value pairs, avoiding duplicate computations. The officially provided disagg_vllm_launcher.sh script shows how to configure a distributed environment in a way that significantly reduces GPU memory usage during cluster deployment.
This answer comes from the articleLMCache: A Key-Value Cache Optimization Tool for Accelerating Reasoning on Large Language ModelsThe
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